Precise, automatic and fast method for vanishing point detection

A new automated approach for vanishing point detection in images of urban scenes is described. This method is based on the theorem of Thales. The main contribution of this paper is the automatic and simultaneous detection of all vanishing points of the image, achieved by converting this problem into the detection of circles in a complex cloud of points, in which each point corresponds to a segment and is associated with an uncertainty. This extraction of circles uses a RANSAC method, modified to improve its speed by using accumulation techniques (Hough transform or otherwise). This robust estimation is then refined by least squares error propagation using the individual variances of each segment. The algorithm is robust, its accuracy is optimised and it is entirely automatic. Its successful operation has been tested on a large number of images of varied urban scenes.     

On the use of iterative re-weighting least-squares and outlier detection for empirically modelling rates of vertical displacement

The proper identification and removal of outliers in the combination of rates of vertical displacements derived from GPS, tide gauges/satellite altimetry, and GRACE observations is presented. Outlier detection is a necessary pre-screening procedure in order to ensure reliable estimates of stochastic properties of the observations in the combined least-squares adjustment (via rescaling of covariance matrices) and to ensure that the final vertical motion model is not corrupted and/or distorted by erroneous data. Results from this study indicate that typical data snooping methods are inadequate in dealing with these heterogeneous data sets and their stochastic properties. Using simulated vertical displacement rates, it is demonstrated that a large variety of outliers (random scattered and adjacent, as well as jointly influential) can be dealt with if an iterative re-weighting least-squares adjustment is combined with a robust median estimator. Moreover, robust estimators are efficient in areas weakly constrained by the data, where even high quality observations may appear to be erroneous if their estimates are largely influenced by outliers. Four combined models for the vertical motion in the region of the Great Lakes are presented. The computed vertical displacements vary between  − 2 mm/year (subsidence) along the southern shores and 3 mm/year (uplift) along the northern shores. The derived models provide reliable empirical constraints and error bounds for postglacial rebound models in the region.     

一种自适应匹配子空间亚像元目标探测方法

为了克服基于线性混合模型的高光谱遥感影像亚像元目标探测方法的缺陷, 提出了一种基于全限制性线性分解的自适应匹配子空间探测方法。首先利用交叉相关光谱匹配技术求得各个像元所含端元类别信息, 然后根据端元类别信息和全限制性分解的结果构造自适应匹配子空间探测算子, 利用端元类别信息在探测中动态选择端元, 降低端元数目估计偏差对探测结果的影响, 提高探测器对目标与背景的可分性。实验证明, 该方法与其他基于线性混合模型的亚像元目标探测方法相比, 可以更好地克服端元数目估计偏差对探测结果的影响, 无论是端元个数低估还是     

自适应空间邻域分析和瑞利-高斯分布的多时相遥感影像变化检测

提出了一种基于自适应空间邻域分析和瑞利-高斯模型(Rayleigh-Gauss models, RGM)分布的多时相遥感影像自动变化检测方法。该方法把自适应空间邻域信息和改进的差值影像与比值影像乘积变换融合 法(improved multiplying transform fusion, IMTF)结合构造差异影像, 可以有效地抑制噪声和消除多时相影像之间配准误差的影响, 具有更强的鲁棒性。在对差异影像的分割处理中, 运用瑞利和高斯模型分别模拟变化类像元和非变化类像元的分布情况, 然后估计出两类像元的概率     

全极化SAR数据反演桥面高度

根据高分辨率SAR图像上建筑区的影像特征, 提出了基于灰度共生矩阵(gray-level cooccurrence Matrix, GLCM)纹理分析的建筑区提取方法, 该方法由初步定位和边界调整2个步骤组成, 均遵循特征计算、基于Bhattacharyya距离的特征选择和KNN分类流程, 所不同的是2个步骤中分别采用了逐块和逐点计算纹理特征的方式以兼顾纹理分析的效率和准确性。文中对不同SAR传感器获取的图像进行了实验。实验结果表明, 选用具有最大Bhattacharyya距离值的3或4个特征可以获得较好的初步定位结果, 建筑区的检测率超过80%, 虚警率低于10%;随着边界调整的进行, 检测到的建筑区边界逐渐接近于真实边界。实验结果验证了该算法的有效性。     

基于边缘分析的海面溢油检测

提出一种基于边缘检测的快速溢油信息提取方法, 首先对溢油图像进行ROA(radio of average)边缘检测, 根据检测结果进行AOI(area of interest)提取, 然后使用改进的Weibull-CFAR检测算法对AOI进行溢油检测, 并与全局CFAR检测结果进行对比。实验结果证明, 所提出的方法对于非均匀灰度SAR图像溢油检测准确性较好、效率较高, 特别适用于大图像的快速溢油检测。     

GeoSurveillance: a GIS-based system for the detection and monitoring of spatial clusters

This article introduces a software package named GeoSurveillance that combines spatial statistical techniques and GIS routines to perform tests for the detection and monitoring of spatial clustering. GeoSurveillance provides both retrospective and prospective tests. While retrospective tests are applied to spatial data collected for a particular point in time, prospective tests attempt to incorporate the dynamic nature of spatial patterns via analyzing time-series data to detect emergent clusters as quickly as possible. This article will outline the structure of GeoSurveillance as well as describe the statistical cluster detection methods implemented in the software. It concludes with an illustration of the use of the software to analyze the spatial pattern of low birth weights in Los Angeles County, California.      

基础地理信息动态更新技术体系初探

近年来,我国测绘部门完成了国家级基础地理信息数据库的"初始建库",基础测绘工作重点已转向持续更新和灵性服务。未来的基础地理信息"应需适时更新"将体现更新内容灵性化、更新周期适时化、更新方式多元化等特点,其主要业务模式包括变化监测、变化测定、主数据库更新和产品更新及服务四大部分。为了有效开展"应需适时更新",应构建包括动态监测与数据预处理、变化信息采集与处理、数据库与产品更新、更新信息网络化服务四大主体部分的动态更新技术体系。该体系是信息化测绘技术体系的一个重要组成部分,应加强对其关键技术的研发,开展技术装备成套化和业务体系建设。     

我国宇宙线空间物理学的进展

本文介绍了宇宙线空间物理学在我国的形成和主要研究内容,包括:(1)空间探测,有火箭探测、卫星探测;地面宇宙线台站观测;宇宙线的水下测量;(2)宇宙线空间物理学研究,有宇宙线的日地传输;宇宙线强度变化与CME事件;宇宙线的源物质与加速过程;宇宙线的地球物理效应.最后提到宇宙线与人体健康,特别是流行性感冒、大的太阳质子事件与地震现象、太阳宇宙线事件与天气和气候等的统计研究.     

气候变化对江河流量变化趋势影响研究进展

气候变化对基于自然稳定气候假定的流量变化趋势的检测和水资源评价方法提出了挑战。在流量变化趋势的检测中分离出气候变化的影响,不仅对水资源管理和水利工程设计有重要的应用价值,而且有助于了解气候变化以何种方式、在何时、何地、已经或尚未对水文循环产生影响,对改进气候模型的模拟与预测有重要的科学价值。 统计方法是检验流量变化趋势显著性的有效工具。直接用气候模型模拟和预测未来径流变化的可靠性取决于模型对当代降水模拟的可信度。多个气候模型集合分析有可能在一定程度上减少模型对降水、径流模拟的不确定性。近年发展起来的多个气候模型集合分析与统计显著性检验技术结合的方法,有可能模拟并预测出气候强迫导致大尺度径流空间分布的变化。随着气候模型尤其是陆—气耦合的区域气候模型对降水模拟的改进,可以预见径流变化的检测、归因和预测的趋同化模拟已为期不远。将温室气体外强迫导致的水文气候变化作为一个因子引入到水资源评价中,对于水资源管理经济与生态评估,以及未来的发展规划将是一件十分重要的变革。 